Summary of Offline Reinforcement Learning with Ood State Correction and Ood Action Suppression, by Yixiu Mao et al.
Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression
by Yixiu Mao, Qi Wang, Chen Chen, Yun Qu, Xiangyang Ji
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach, SCAS, addresses a previously underexplored issue in offline reinforcement learning (RL) – the out-of-distribution (OOD) state problem. Unlike existing methods that focus on OOD action suppression, SCAS unifies OOD state correction and OOD action suppression to improve performance. The technique achieves value-aware OOD state correction, which corrects the agent from OOD states to high-value in-distribution states. Experimental results show excellent performance on standard offline RL benchmarks without additional hyperparameter tuning. Furthermore, SCAS demonstrates enhanced robustness against environmental perturbations due to its OOD state correction feature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning (RL) has a new problem to solve: out-of-distribution (OOD) states! When an agent encounters states not in the training data during testing, it can’t behave well and performance suffers. The solution is SCAS, a simple but effective approach that fixes this issue. It’s like a GPS that helps the agent find its way back to good behavior when it gets lost. SCAS works really well on standard offline RL tests and even does better with unexpected changes in the environment. |
Keywords
» Artificial intelligence » Hyperparameter » Reinforcement learning
